

"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class      _UpperCAmelCase      (		__lowerCAmelCase):
			__a			:							Optional[int]					       =				(KDPMaDiscreteScheduler,)
			__a			:							str					       =				1_0
			def 				__snake_case			(		self   ,		**_A  )     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[int]          =       {
				    """num_train_timesteps""": 11_00,
				    """beta_start""": 0.0001,
				    """beta_end""": 0.02,
				    """beta_schedule""": """linear""",
				}

				config.update(**_lowerCamelCase  )
				return config
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]   ,		[0.0002, 0.002, 0.02]  ):
					self.check_over_configs(beta_start=_lowerCamelCase   ,		beta_end=_lowerCamelCase  )
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_lowerCamelCase  )
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_lowerCamelCase  )
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				List[Any]          =       self.scheduler_classes[0]
				_UpperCAmelCase     :				List[Any]          =       self.get_scheduler_config(prediction_type="""v_prediction"""  )
				_UpperCAmelCase     :				str          =       scheduler_class(**_lowerCamelCase  )

				scheduler.set_timesteps(self.num_inference_steps  )

				_UpperCAmelCase     :				Dict          =       self.dummy_model()
				_UpperCAmelCase     :				Any          =       self.dummy_sample_deter * scheduler.init_noise_sigma
				_UpperCAmelCase     :				Optional[Any]          =       sample.to(_lowerCamelCase  )

				for i, t in enumerate(scheduler.timesteps  ):
					_UpperCAmelCase     :				int          =       scheduler.scale_model_input(_lowerCamelCase   ,		_lowerCamelCase  )

					_UpperCAmelCase     :				Any          =       model(_lowerCamelCase   ,		_lowerCamelCase  )

					_UpperCAmelCase     :				Any          =       scheduler.step(_lowerCamelCase   ,		_lowerCamelCase   ,		_lowerCamelCase  )
					_UpperCAmelCase     :				int          =       output.prev_sample

				_UpperCAmelCase     :				Optional[Any]          =       torch.sum(torch.abs(_lowerCamelCase  )  )
				_UpperCAmelCase     :				str          =       torch.mean(torch.abs(_lowerCamelCase  )  )

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 4.69_34e-07  ) < 1e-2
					assert abs(result_mean.item() - 6.11_12e-10  ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07  ) < 1e-2
					assert abs(result_mean.item() - 0.0002  ) < 1e-3
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				if torch_device == "mps":
					return
				_UpperCAmelCase     :				str          =       self.scheduler_classes[0]
				_UpperCAmelCase     :				str          =       self.get_scheduler_config()
				_UpperCAmelCase     :				int          =       scheduler_class(**_lowerCamelCase  )

				scheduler.set_timesteps(self.num_inference_steps  )

				_UpperCAmelCase     :				List[Any]          =       self.dummy_model()
				_UpperCAmelCase     :				Tuple          =       self.dummy_sample_deter * scheduler.init_noise_sigma
				_UpperCAmelCase     :				int          =       sample.to(_lowerCamelCase  )

				for i, t in enumerate(scheduler.timesteps  ):
					_UpperCAmelCase     :				List[Any]          =       scheduler.scale_model_input(_lowerCamelCase   ,		_lowerCamelCase  )

					_UpperCAmelCase     :				Dict          =       model(_lowerCamelCase   ,		_lowerCamelCase  )

					_UpperCAmelCase     :				Union[str, Any]          =       scheduler.step(_lowerCamelCase   ,		_lowerCamelCase   ,		_lowerCamelCase  )
					_UpperCAmelCase     :				Optional[int]          =       output.prev_sample

				_UpperCAmelCase     :				Optional[int]          =       torch.sum(torch.abs(_lowerCamelCase  )  )
				_UpperCAmelCase     :				str          =       torch.mean(torch.abs(_lowerCamelCase  )  )

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 20.41_25  ) < 1e-2
					assert abs(result_mean.item() - 0.0266  ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 20.41_25  ) < 1e-2
					assert abs(result_mean.item() - 0.0266  ) < 1e-3
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				if torch_device == "mps":
					return
				_UpperCAmelCase     :				List[str]          =       self.scheduler_classes[0]
				_UpperCAmelCase     :				str          =       self.get_scheduler_config()
				_UpperCAmelCase     :				int          =       scheduler_class(**_lowerCamelCase  )

				scheduler.set_timesteps(self.num_inference_steps   ,		device=_lowerCamelCase  )

				_UpperCAmelCase     :				Optional[int]          =       self.dummy_model()
				_UpperCAmelCase     :				int          =       self.dummy_sample_deter.to(_lowerCamelCase  ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					_UpperCAmelCase     :				Dict          =       scheduler.scale_model_input(_lowerCamelCase   ,		_lowerCamelCase  )

					_UpperCAmelCase     :				List[Any]          =       model(_lowerCamelCase   ,		_lowerCamelCase  )

					_UpperCAmelCase     :				Any          =       scheduler.step(_lowerCamelCase   ,		_lowerCamelCase   ,		_lowerCamelCase  )
					_UpperCAmelCase     :				str          =       output.prev_sample

				_UpperCAmelCase     :				List[str]          =       torch.sum(torch.abs(_lowerCamelCase  )  )
				_UpperCAmelCase     :				Union[str, Any]          =       torch.mean(torch.abs(_lowerCamelCase  )  )

				if str(_lowerCamelCase  ).startswith("""cpu"""  ):
					# The following sum varies between 148 and 156 on mps. Why?
					assert abs(result_sum.item() - 20.41_25  ) < 1e-2
					assert abs(result_mean.item() - 0.0266  ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 20.41_25  ) < 1e-2
					assert abs(result_mean.item() - 0.0266  ) < 1e-3






"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   float:




						'''simple docstring'''



						return price * (1 + tax_rate)


if __name__ == "__main__":
	print(F'''{price_plus_tax(100, 0.25) = }''')
	print(F'''{price_plus_tax(125.50, 0.05) = }''')


